Advertisers want to prosecute advertising campaigns for specific purposes. Sometimes the purpose of such campaigns demands reaching target audiences sharing particular attributes (e.g., “women with a college degree in the 24-28 age range living in California”, etc.) in order to optimize the effectiveness of the campaign. In some cases, lists of audience members sharing such attributes can be compiled by querying online data recorded for those members (e.g., online users). For example, a woman of age 25 living in Manhattan Beach, Calif. might visit an online website, and in the course of her online visit, she might accept a cookie or otherwise leave a record of her online visit. Such a cookie or other record of her online visit might be stored and accessed at a later moment in time. In many cases, her online visit might also immediately trigger some action related to a campaign (e.g., check for a match of the user to the campaign target audience). In some cases, an advertising campaign might include user data that is only available from offline activity (e.g., a user makes a purchase at the advertiser's store or kiosk). Audience list constituents (e.g., targeted candidates) for an advertising campaign may be described at least in part by offline data. For example, offline data might be collected at a brick-and-mortar retail store (e.g., in-store purchase records, point-of-sale rewards program registration, etc.). Such offline user data can be combined with online user data associated with the same user, and the combination can be used in a query to generate an audience list.
In some legacy situations, offline user data can be combined with online user data whenever the user browses the advertiser's site. When an advertiser seeks to prosecute a campaign, the advertiser often wants to ramp up the campaign quickly, reaching a maximum highly-targeted audience volume in a short period of time. However, is it quite possible that not all of the potential candidates for the campaign will login or otherwise appear in online setting at or near the time of the campaign launch so as to trigger combining offline data with online data so as to update the user's profile and/or the user's demographics. What is needed is a technique or techniques for efficiently and quickly processing voluminous amounts of user data to simulate online user visits so as to trigger combining offline data with online data so as to update the user's offline data, and so as to quickly ramp-up an advertising campaign with current demographics.
None of the aforementioned legacy approaches achieve the capabilities of the herein-disclosed techniques for iterating through a set of user web page visits and simulating new user web page visits to generate an advertising campaign target audience list. Therefore, there is a need for improvements.